30 Apr 2026
4 min read
GenAI is everywhere right now, but real results are harder to find. Many organizations jump in quickly, without first getting clear on what they’re trying to solve or how success will be measured.
In a recent TAG Data Talk episode sponsored by Wavicle, Dr. Beverly Wright talks with Kelly Moncrief about why so many GenAI initiatives stall after early experimentation.
Here are five key lessons from the conversation on what it takes to turn GenAI into meaningful business impact.
Speaker details
- Host: Dr. Beverly Wright – Executive in Residence, Institute for Insight, Georgia State University
- Guest speaker: Kelly Moncrief – Head of AI Technology, Southern Company
1. The “shiny object” appeal of GenAI can quickly derail focus
GenAI captures attention because it feels futuristic, powerful, and transformative. Kelly explains that when most people talk about AI, they’re reacting less to practical applications and more to cultural narratives shaped by media, science fiction, and rapid technological advancement.
“When we think about AI, it’s this intersection of like fantastical and futuristic environments… it was this thing of the future, and now all of a sudden it’s real.”
From viral deepfakes to highly produced vendor demos, AI quickly becomes something exciting to discuss. Kelly notes that this excitement often pulls organizations away from the harder but more important work of defining why AI is needed at all.
“You add all that together and you get really shiny, sparkly tools that are just really cool to talk about.”
2. The trap of doing AI without a purpose
Market momentum and fear of missing out continue to drive GenAI adoption, sometimes at the expense of thoughtful evaluation. Kelly points to industry data showing just how widespread AI experimentation has become, increasing the pressure to follow suit.
“Last year, McKinsey had a stat that said over 65% of Fortune 500 companies had already announced partnerships or pilots with the hyperscalers.”
When nearly every solution is marketed as AI-powered, it becomes difficult to slow down and assess whether AI is actually needed or if it is simply being adopted to keep pace.
3. Strong use cases must come before technology decisions
A central theme of the conversation is the importance of reversing the typical approach to innovation. Kelly stresses that organizations should start with business problems and measurable outcomes before deciding whether AI is the right tool.
“Have a business pain point that you’re solving for or a metric that you’re trying to move and do that first.”
Approaching AI as one option among many helps organizations avoid overengineering solutions and improves the likelihood that initiatives create real value.
4. Lack of alignment early on leads to stalled AI initiatives
When expectations and success criteria are not defined up front, AI projects often struggle later. Kelly describes how initiatives can lose momentum when stakeholders have not agreed on what success looks like or how performance should be measured.
“Nobody agreed at the origin of the solution: what are we solving for, and what needle are we moving?”
Clear alignment at the beginning helps set realistic expectations, secures long-term support, and prevents initiatives from stalling during the move to production.
5. Scaling AI requires prioritization, not less innovation
Kelly describes that being disciplined about AI adoption does not mean slowing innovation. Instead, it means focusing limited resources on initiatives that deliver the greatest impact.
“Convert that tunnel into a funnel and make sure the most valuable initiatives come out the other side.”
Applying clear filters based on value, impact, and cost allows organizations to scale AI more effectively while still encouraging experimentation in the right places.
Final takeaway
This conversation underscores one core idea: GenAI creates real value only when it is anchored in clear use cases, aligned to business outcomes, and supported by long-term commitment. Innovation matters, but without alignment, clear business outcomes, and a value-first mindset, even the most advanced AI initiatives struggle to make it to production. GenAI success isn’t a destination; it’s a disciplined approach to solving real problems at scale.
Watch the full podcast below:
Looking to get more value from your data and AI initiatives? Wavicle can help you cut through the noise and focus on what actually works. Connect with us to get started.
Disclaimer: Quotes in this blog are excerpted from a longer conversation and have been edited for length and clarity.
